Show code
knitr::include_graphics("preview.jpg")
In this short article I am exploring what the Dutch political landscape would look like if it operated under a first-past-the-post (FPTP) system.
The election data for the Netherlands was downloaded from the Kiesraad. The shapefile was downloaded from ESRI NL.
Results Tweede Kamer Verkiezing 2021
These are the 2021 results summarised:
Show code
#sort in long format
df_results[is.na(df_results)] <- 0
df_pr <- df_results %>%
pivot_longer(cols = VVD:Modern.Nederland, names_to = "party", values_to = "votes") %>%
select(party, votes) %>%
group_by(party) %>%
summarise(votes=sum(votes))
df_pr$party <- gsub("Partij.van.de.Arbeid..P.v.d.A..","PvdA",df_pr$party)
df_pr$party <- gsub("Democraten.66..D66.","D66",df_pr$party)
df_pr$party <- gsub("X50PLUS","Partij 50PLUS",df_pr$party)
df_pr$party <- gsub("Forum.voor.Democratie","FvD",df_pr$party)
df_pr$party <- gsub("Forum.voor.Democratie","FvD",df_pr$party)
df_pr$party <- gsub("SP..Socialistische.Partij.","SP",df_pr$party)
df_pr$party <- gsub("PVV..Partij.voor.de.Vrijheid.","PVV",df_pr$party)
df_pr$party <- gsub("Staatkundig.Gereformeerde.Partij..SGP.","SGP",df_pr$party)
df_pr$party <- gsub("Partij.voor.de.Dieren","PvdD",df_pr$party)
dHondt <- function(votes, parties, n_seats = 150) {
divisor.mat <- sum(votes) / sapply(votes, "/", seq(1, n_seats, 1))
colnames(divisor.mat) <- parties
m.mat <- tidyr::gather(as.data.frame(divisor.mat), key="name", value="value",
everything())
m.mat <- m.mat[rank(m.mat$value, ties.method = "random") <= n_seats, ]
rle.seats <- rle(as.character(m.mat$name))
if (sum(rle.seats$length) != n_seats)
stop(paste("Number of seats distributed not equal to", n_seats))
# fill up the vector with parties that got no seats
if (any(!(parties %in% rle.seats$values))) {
# add parties
missing_parties <- parties[!(parties %in% rle.seats$values)]
for (party in missing_parties) {
rle.seats$lengths <- c(rle.seats$lengths, 0)
rle.seats$values <- c(rle.seats$values, party)
}
# sort results
rle.seats$lengths <- rle.seats$lengths[match(parties, rle.seats$values)]
rle.seats$values <- rle.seats$values[match(parties, rle.seats$values)]
}
rle.seats$length
}
df_pr$seats<- dHondt(df_pr$votes,df_pr$party,150)
df_pr <- df_pr %>%
filter(seats>0) %>%
as.data.frame()
df_pr <- df_pr[order(df_pr$votes,decreasing =T),]
df_pr <- left_join(df_pr,colourscheme)
df_pr$total <- sum(df_pr$votes)
df_pr$percentage <- (df_pr$votes/df_pr$total)*100
df_pr$percentage <- round(df_pr$percentage,1)
resultaat <- df_pr %>%
select(party,votes,seats,percentage)
kable(resultaat)
| party | votes | seats | percentage |
|---|---|---|---|
| VVD | 2279126 | 34 | 22.3 |
| D66 | 1565862 | 24 | 15.3 |
| PVV | 1124482 | 17 | 11.0 |
| CDA | 990601 | 15 | 9.7 |
| SP | 623371 | 9 | 6.1 |
| PvdA | 597192 | 9 | 5.8 |
| GROENLINKS | 537308 | 8 | 5.3 |
| FvD | 523083 | 8 | 5.1 |
| PvdD | 399751 | 6 | 3.9 |
| ChristenUnie | 351275 | 5 | 3.4 |
| Volt | 252480 | 3 | 2.5 |
| JA21 | 246620 | 3 | 2.4 |
| SGP | 215249 | 3 | 2.1 |
| DENK | 211238 | 3 | 2.1 |
| Partij 50PLUS | 106702 | 1 | 1.0 |
| BBB | 104319 | 1 | 1.0 |
| BIJ1 | 87238 | 1 | 0.9 |
Let’s start with examining the composition of the Tweede Kamer - under a proportional representation (D’Hondt) system. This is what the Tweede Kamer looks like currently:
Show code
ggplot(df_pr) +
ggpol::geom_parliament(aes(seats = seats, fill = party),color="black") +
#highlight the party in control of the House with a black line
scale_fill_manual(values = df_pr$colour,
labels = df_pr$party)+
coord_fixed()+
theme_void()
Now let’s have a look at what it would look like under First-past-the-Post (FPTP), with one MP elected per Gemeente.
Show code
#sort in long format
df_long <- df_results %>%
pivot_longer(cols = VVD:Modern.Nederland, names_to = "party", values_to = "votes") %>%
select(regio =RegioNaam, code = RegioCode,party, votes) %>%
filter(!is.na(votes)) %>%
group_by(code) %>%
mutate(total = sum(votes)) %>%
slice_max(votes)
df_long$percentage <- (df_long$votes/df_long$total)*100
df_long$percentage <- round(df_long$percentage,1)
df_long$code <- gsub("G","",df_long$code)
#fix the names
df_long$party <- gsub("Partij.van.de.Arbeid..P.v.d.A..","PvdA",df_long$party)
df_long$party <- gsub("Democraten.66..D66.","D66",df_long$party)
df_long$party <- gsub("X50PLUS","Partij 50PLUS",df_long$party)
df_long$party <- gsub("Forum.voor.Democratie","FvD",df_long$party)
df_long$party <- gsub("Forum.voor.Democratie","FvD",df_long$party)
df_long$party <- gsub("SP..Socialistische.Partij.","SP",df_long$party)
df_long$party <- gsub("PVV..Partij.voor.de.Vrijheid.","PVV",df_long$party)
df_long$party <- gsub("Staatkundig.Gereformeerde.Partij..SGP.","SGP",df_long$party)
df_long$party <- gsub("Partij.voor.de.Dieren","PvdD",df_long$party)
df_long$party <- as.factor(df_long$party)
df_long$regio <- gsub("'","",df_long$regio)
df_long$regio <- gsub("-"," ",df_long$regio)
df_long <- left_join(df_long,colourscheme)
fptp_results <- table(df_long$party) %>% as.data.frame()
colnames(fptp_results) <- c("party","seats")
fptp_results <- left_join(fptp_results,colourscheme)
ggplot(fptp_results) +
ggpol::geom_parliament(aes(seats = seats, fill = party),color="black") +
#highlight the party in control of the House with a black line
scale_fill_manual(values = fptp_results$colour,
labels = fptp_results$party)+
coord_fixed()+
theme_void()
Now, there are a couple of obvious differences:
- The VVD has an absolute majority, despite only having 22.3% of the vote.
- There are a lot more seats in this fictional house.
But also some obvious caveats:
- Each Gemeente has a different population, but here Amsterdam and Appingedam are represented equally. In the UK, with its FPTP system, constituencies are somewhat based on population. For example London has 73 MPs, while Scotland has 59 MPs.
Election results on a map
We can also show these results on a map - this in an interactive map which allows you to explore the individual election results for each Gemeente.
Show code
#sort in long format
df_pie <- df_results %>%
pivot_longer(cols = VVD:Modern.Nederland, names_to = "party", values_to = "votes") %>%
select(regio =RegioNaam, code = RegioCode,party, votes) %>%
filter(!is.na(votes)) %>%
group_by(code) %>%
mutate(total = sum(votes))
df_pie$code <- gsub("G","",df_pie$code)
names_df <- unique(df_pie$code)
names_shp <- unique(shp_gemeentes$Gemeenteco)
names_remove <- names_df[!names_df %in% names_shp]
#remove Bonaire, Saba, Sint Eustasius
df_pie <- df_pie %>%
filter(!code %in% names_remove)
df_pie$percentage <- (df_pie$votes/df_pie$total)*100
df_pie$percentage <- round(df_pie$percentage,1)
#
df_pie$party <- gsub("Partij.van.de.Arbeid..P.v.d.A..","PvdA",df_pie$party)
df_pie$party <- gsub("Democraten.66..D66.","D66",df_pie$party)
df_pie$party <- gsub("X50PLUS","Partij 50PLUS",df_pie$party)
df_pie$party <- gsub("Forum.voor.Democratie","FvD",df_pie$party)
df_pie$party <- gsub("Forum.voor.Democratie","FvD",df_pie$party)
df_pie$party <- gsub("SP..Socialistische.Partij.","SP",df_pie$party)
df_pie$party <- gsub("PVV..Partij.voor.de.Vrijheid.","PVV",df_pie$party)
df_pie$party <- gsub("Staatkundig.Gereformeerde.Partij..SGP.","SGP",df_pie$party)
df_pie$party <- gsub("Partij.voor.de.Dieren","PvdD",df_pie$party)
df_pie$party <- as.factor(df_pie$party)
df_pie$party <- as.character(df_pie$party)
df_pie$percentage <- as.numeric(df_pie$percentage)
pie_plot_list <- lapply(unique(df_pie$code), function(i) {
regio_df <- df_pie %>%
filter(code == i) %>%
dplyr::ungroup() %>%
select(party,percentage,regio) %>%
filter(percentage >0.1) %>%
as.data.frame()
regio_df <- regio_df[order(regio_df$percentage,decreasing = T),]
regio_df <- left_join(regio_df,colourscheme)
ggplot(regio_df) +
geom_bar(aes(x = "", y = percentage, fill = party),
stat = "identity", colour = "black", width =1) +
#geom_text(aes(x = "", y = pct, label = percent(pct)), position = position_stack(vjust = 0.5))+
coord_polar("y", start = 0) +
labs(x = NULL, y = NULL, fill = NULL,
title = paste("Verkiezingsuitslag",unique(regio_df$regio))) +
theme_classic()+
scale_fill_manual(values = regio_df$colour,
limits = regio_df$party)+
theme(axis.line = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
plot.title = element_text(hjust = 0.5, color = "black"),
legend.position = "bottom")
})
#remove saba, st eustasius etc.
df_long <- df_long %>%
filter(!code %in% names_remove)
#link to spatial
shp_fptp <- left_join(shp_gemeentes, df_long, by = c("Gemeenteco"="code"))
shp_fptp <- shp_fptp %>%
filter(percentage >0.1)
#I need to make the shapefile alphabetical to match with the pie chart list
#This means removing apostrophe's at 's-Gravenhage and 's-Hertogenbosch
shp_fptp$Gemeentena <- gsub("'","",shp_gemeentes$Gemeentena)
#reorder
shp_fptp <- shp_fptp[order(shp_fptp$Gemeentena),]
#reset rownumbers so they line up
rownames(shp_fptp) <- 1:nrow(shp_fptp)
mapview(shp_fptp,
zcol = "party",
col.regions = shp_fptp$colour,
popup = popupGraph(pie_plot_list, width = 450,height =300))